Preplant and Postemergence Control of Glyphosate-Resistant Giant Ragweed in Corn
Bibliographic record
Abstract
Glyphosate-resistant (GR) giant ragweed has recently been identified in southwestern Ontario and has the potential to be a significant problem for regional corn producers. Eight field trials [four with preplant (PP) and four with postemergence (POST) herbicides] were conducted from 2013 to 2014 on various Ontario farms infested with GR giant ragweed to determine the efficacy of PP and POST tank-mixes in corn. Glyphosate tank-mixed with atrazine, dicamba, dicamba/atrazine, mesotrione plus atrazine, flumetsulam, isoxaflutole plus atrazine, saflufenacil/dimethenamid-P, S-metolachlor/atrazine and rimsulfuron applied PP provided up to 54%, 95%, 93%, 95%, 40%, 89%, 91%, 50% and 93% control of GR giant ragweed and reduced dry weight 69%, 100%, 99%, 100%, 30%, 92%, 98%, 66% and 99%, respectively. POST application of glyphosate alone and tank-mixed with 2,4-D ester, atrazine, dicamba, dicamba/diflufenzopyr, dicamba/atrazine, bromoxynil plus atrazine, prosulfuron plus dicamba, mesotrione plus atrazine, topramezone plus atrazine, tembotrione/thiencarbazone-methyl and glufosinate provided up to 31%, 84%, 39%, 94%, 89%, 86%, 83%, 78%, 72%, 43%, 63% and 58% GR giant ragweed and reduced dry weight 55%, 99%, 72%, 99%, 99%, 98%, 96%, 96%, 93%, 89%, 91% and 95%, respectively. In general, PP control of GR giant ragweed was greater than POST applied herbicides evaluated. Based on these results, glyphosate tank-mixes containing dicamba or mesotrione plus atrazine applied PP, and dicamba applied POST will provide the most consistent control of GR giant ragweed in corn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".